Thursday, October 11, 2018
02:00 PM - 03:00 PM
What’s happened with data organization?
The rise of “big data” gave rise to flat data lake architectures. Flat data lake architectures are generally preferred for machine learning applications. But, facing ever-growing data volumes, many organizations are still struggling with answering basic questions for simply running the business:
- How many customers did I add/lose last week?
- Did we meet our revenue forecasts last quarter?
These fundamental questions are usually better suited for traditional Business Intelligence tools. But can traditional BI tools and AI/machine learning tools use the same data structures efficiently?
Yes, with thoughtful data architecture, it’s possible.
In this talk, we will explore data architectures that make sense for both data scientists and business intelligence analysts. There’s no need to replicate data to serve two functions; one model can serve many.
Mr. Walrond is an experienced Systems and Data Architect focusing on helping enterprises design and implement data analytic solutions in the cloud having personally led several teams in cloud data implementations. Mr. Walrond's experience offers first-hand knowledge of what works and what doesn't when implementing repeatable and sustainable cloud analytics solutions. Mr. Walrond has served as Principle Consultant at Caserta since January 2016. Prior to Caserta, Mr. Walrond was with Context Relevant, an innovative startup with a revolutionary distributed, self-optimizing machine learning product implementing predictive analytics solutions in the Financial Services sector. Mr. Walrond has also held several leadership roles in enterprise data analytics at Scholastic, Dun & Bradstreet, and Deloitte Consulting.